Citing

AstroML is a Python module for machine learning and data mining built on
numpy,
scipy,
scikit-learn,
matplotlib,
and astropy,
and distributed under the 3-clause BSD license.
It contains a growing library of statistical and machine learning
routines for analyzing astronomical data in Python, loaders for several open
astronomical datasets, and a large suite of examples of analyzing and
visualizing astronomical datasets.

The goal of astroML is to provide a community repository for fast Python
implementations of common tools and routines used for statistical
data analysis in astronomy and astrophysics, to provide a uniform and
easy-to-use interface to freely available astronomical datasets.
We hope this package will be useful to researchers and students of
astronomy. If you have an example you’d like to share, we are happy to
accept a contribution via a GitHub
Pull Request:
the code repository can be found at
http://github.com/astroML/astroML.

The astroML project was started in 2012 to accompany the book
Statistics, Data Mining, and Machine Learning in Astronomy by
Zeljko Ivezic, Andrew Connolly, Jacob VanderPlas, and Alex Gray,
published by
Princeton University Press.
The table of contents is available
here(pdf),
or you can preview or purchase the book on
Amazon.

Did you find a mistake or typo in the book? We maintain an up-to-date
listing of errata
in the text which you can view on GitHub. If you find a mistake
which is not yet noted on that page, please let us know via email or GitHub
pull request!